Review and Assessment of Stress-Based Multiaxial Fatigue Models for High Cycle Fatigue Life Predictions
Bibliographic record
Abstract
In a previous study [1], several multiaxial fatigue models were investigated and compared based on their ability to predict the fatigue limit under multiaxial loading conditions. The widely used historical models such as Findley [2] and Dang Van [3] were compared to several recently developed models. The methods were investigated for the purpose of assessing their potential use in automotive design. In the current study, the same multiaxial fatigue models were assessed based on their ability to perform life prediction under high cycle multiaxial loading. The experimental data used for the assessment of the seven different multiaxial models was taken from literature. Five of the models, Findley, McDiarmid, Susmel-Lazzarin, MZSL, and scaled normal stress were critical plane approaches. The other two models were the LTJ approach and the prismatic hull method, both of which are based on the von Mises criteria. The scaled normal stress approach was the only tensile failure mode model investigated with all other models being shear failure mode. Each stress-based model was used to predict the fatigue life and compare to the experimental results obtained from literature. When selecting data from literature, only high cycle multiaxial fatigue data was used. Experimental data from steel, stainless steel, and aluminum materials were investigated. Most of the materials exhibited shear failure mode, but some materials had mixed mode cracking. The models were judged based on their ability to predict the multiaxial fatigue life within factors of 3 and 5. The LTJ model had the best overall agreement with the experimental data, with 82% of life predictions within a factor of 3 and 96% within a factor of 5. This was due in part to its material parameter, which is derived from multiaxial test data. The LTJ model was one of two models that required multiaxial test data to generate a model parameter. All other models relied on either monotonic or uniaxial fatigue data, which is more readily available and much easier to generate. The Susmel-Lazzarin approach had the second-best overall agreement with 60% and 79% of predictions within factors of 3 and 5, respectively. The scaled normal stress approach, prismatic hull approach, and MZLS approach had life predications that were just slightly less accurate than the Susmel-Lazzarin method. The McDiarmid and Findley models had the worst correlation with the experimental data investigated in this study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".